We’re looking for 2 interns for our company to help us in R&D. Send us an email to nurdin@soding.com.my, put subject “Soding – R&D position” and write an essay why you want to work with us (min 200 words).

We’re looking for 2 interns for our company to help us in business development. Send us an email to nurdin@soding.com.my, put subject “Soding – BD position” and write an essay why you want to work with us (min 200 words).

Create a new Jupyter notebook with python 2.7 kernel. Name it as TensorFlow RNN – model. In this tutorial we will train chat conversation. There are two phases in this tutorial, training the modelling and test the chat response. Import libraries and modules.

context = {}
ERROR_THRESHOLD = 0.25
def classify(sentence):
# generate probabilities from the model
results = model.predict([bow(sentence, words)])[0]
# filter out predictions below a threshold
results = [[i,r] for i,r in enumerate(results) if r>ERROR_THRESHOLD]
# sort by strength of probability
results.sort(key=lambda x: x[1], reverse=True)
return_list = []
for r in results:
return_list.append((classes[r[0]], r[1]))
# return tuple of intent and probability
return return_list
def response(sentence, userID='123', show_details=False):
results = classify(sentence)
# if we have a classification then find the matching intent tag
if results:
# loop as long as there are matches to process
while results:
for i in intents['intents']:
# find a tag matching the first result
if i['tag'] == results[0][0]:
# set context for this intent if necessary
if 'context_set' in i:
if show_details: print ('context:', i['context_set'])
context[userID] = i['context_set']
# check if this intent is contextual and applies to this user's conversation
if not 'context_filter' in i or \
(userID in context and 'context_filter' in i and i['context_filter'] == context[userID]):
if show_details: print ('tag:', i['tag'])
# a random response from the intent
print (random.choice(i['responses']))
results.pop(0)

60,000 samples in the training set, and the images are 28 pixels x 28 pixels each. Plotting the first sample in matplotlib

from matplotlib import pyplot as plt
plt.imshow(X_train[0])

Preprocess input data for Keras. When using the Theano backend, you must explicitly declare a dimension for the depth of the input image. For example, a full-color image with all 3 RGB channels will have a depth of 3. Our MNIST images only have a depth of 1, but we must explicitly declare that. In other words, we want to transform our dataset from having shape (n, width, height) to (n, depth, width, height).

Learn Tensorflow in 1 day
====================
Join my Tensorflow workshop and learn how to explore & analyze dataset including image and text in just 1 day. Only basic programming knowledge is required.

Create a new Jupyter notebook with python 2.7 kernel. Name it as TensorFlow CNN. In this tutorial we will train a simple classifier to classify images of birds. Open your Chrome browser and install Fatkun Batch Download Image. Google this keyword malabar pied hornbill. Select Images and click Fatkun Batch Download Image icon on the right top. Select This tab and new windows will appear.

Fatkun Batch Download Image

Unselect which images that not related to malabar pied hornbill bird category then click Save Image. Make sure minimum images that need to be train is 75. Wait until all images finish download. Copy all the images and place it into <your_working_space> > tf_files > birds > images > Malabar Pied Hornbill. Repeat the same steps over and over again for these categories.

Define a neural network architecture with 3 layers; input, hidden and output. The number of neurons in input and output are fixed, as the input is our 28 x 28 image and the output is a 10 x 1 vector representing the class. We take 500 neurons in the hidden layer. This number can vary according to your need. We also assign values to remaining variables.

tf.add(x, y)
Add two tensors of the same type, x + y
tf.sub(x, y)
Subtract tensors of the same type, x — y
tf.mul(x, y)
Multiply two tensors element-wise
tf.pow(x, y)
Take the element-wise power of x to y
tf.exp(x)
Equivalent to pow(e, x), where e is Euler’s number (2.718…)
tf.sqrt(x)
Equivalent to pow(x, 0.5)
tf.div(x, y)
Take the element-wise division of x and y
tf.truediv(x, y)
Same as tf.div, except casts the arguments as a float
tf.floordiv(x, y)
Same as truediv, except rounds down the final answer into an integer
tf.mod(x, y)
Takes the element-wise remainder from division

Create a new Jupyter notebook with python 2.7 kernel. Name it as TensorFlow operators. Lets write a small program to add two numbers.

Join my Android development workshop and learn how to build an app and release to app store in just 2 days. Only basic programming knowledge is required.

Upon completion of this course, you will be able to:
a. Build your own applications for Android mobile phones.
b. Understand how Android applications work, Android application components, manifest and Intent
c. Design Android applications with compelling user interfaces by using and creating your own layouts using external resources.
d. Use Android Web APIs for assessing Web Services such as Twitter etc. in background services
e. Take advantage of Android APIs for data storage and retrieval via user preferences, files and local databases.
f. Utilize the powerful Android API on maps, speech, medias and hardware to build complex applications.
g. Debug, test and deploy your own applications on Google Play Store.

Course fee is only RM550/pax (course materials and meals are included). Interested? Fill in your details through below link and I’ll be in touch soon: